from IPython.display import HTML
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def showImagesMatrix(list_of_files,hSize, wSize,col=10):
fig = figure( figsize=(wSize, hSize))
number_of_files = len(list_of_files)
row = number_of_files/col
if (number_of_files%col != 0):
row += 1
for i in range(number_of_files):
a=fig.add_subplot(row,col,i+1)
image = imread(mypath+'/'+list_of_files[i])
imshow(image,cmap='Greys_r')
axis('off')
from IPython.display import Image
PATH = "E:/FS_meeting/FiguresMisc/"
Image(filename = PATH + "Apps_Long.jpg", width=1000, height=1000)
The final metadata and choices can be found in the CleandupNECNdocumentation.csv file
PATH = "E:/FS_meeting/FiguresMisc/"
Image(filename = PATH + "Davis2009.PNG", width=1000, height=1000)
</ul>
Other values were taken from existing LANDIS papers, these can be found in the NECN folder. Species that could not be found in either way were adapted from qualitative assessment of range in comparison to know values for other species.
I ran several differnt trials with differnt variables to see what the differnt groups looked like. I chose min ppt, min VPD, mean temp and max elevation and ran a mean shift algorithm at different band widths until I found 3-4 groups. For the Conifers three groups fit well. For hardwoods there were a few species that fit no group. Because of how mean shift works expanding groups to include them collapsed too much of the difference between groups. In essence you can have 8 groups or 1 groups. I assigned these to the group closest too them, ending with 4 groups.
from matplotlib.pyplot import figure, imshow, axis
from matplotlib.image import imread
mypath='E:/FS_meeting/FiguresMisc'
hSize = 30
wSize = 50
col = 3
Important_Species=[129,621,832,316,806,833,132,711,802,261]
ConifersFunc=["Conifer1.jpg","Conifer2.jpg","Conifer3.jpg","ConiferPCA.jpg"]
showImagesMatrix(ConifersFunc, hSize = 20, wSize = 20,col=2)
from matplotlib.pyplot import figure, imshow, axis
from matplotlib.image import imread
mypath='E:/FS_meeting/FiguresMisc'
hSize = 30
wSize = 50
col = 3
Important_Species=[129,621,832,316,806,833,132,711,802,261]
ConifersFunc=["Hardwoods1 at1.2.jpg","Hardwoods2 at1.2.jpg","Hardwoods3 at1.2.jpg","HardwoodsPCA at1.2.jpg"]
showImagesMatrix(ConifersFunc, hSize = 20, wSize = 20,col=2)
Conifers
from matplotlib.pyplot import figure, imshow, axis
from matplotlib.image import imread
mypath='E:/Species Imput Files_1_3/Max_AGB'
hSize = 30
wSize = 50
col = 3
Important_Species=[129,621,832,316,806,833,132,711,802,261]
Growthcurveplot=["129_svp.jpeg","621_svp.jpeg","832_svp.jpeg","832_svp.jpeg","316_svp.jpeg","806_svp.jpeg","833_svp.jpeg",
"132_svp.jpeg","711_svp.jpeg","802_svp.jpeg","261_svp.jpeg",]
showImagesMatrix(Growthcurveplot, hSize = 62, wSize = 20,col=2)
Now that we have seen what some values for the maximum amount of Biomass we want to get a better idea for what the growth curves should look like so that we can calibarte the growth over time. To do this we use a "" associaation to look at the sight index and height to relate to age. Then we will look at the upper 20% of sites a fit a logarithmic regression to simulate the growht patterns of trees. This will be used as a basis for LANDIS-II Trials to tune the rate of growth.
Here are graphs the 9 most prevelant species on the landscape. For each functional group we will use the two most prevalent species and find parameters that best align with the growth curve.
from matplotlib.pyplot import figure, imshow, axis
from matplotlib.image import imread
mypath='E:/Growth_cruves/Output'
hSize = 30
wSize = 50
col = 3
Important_Species=[129,621,832,316,806,833,132,711,802,261]
Growthcurveplot=["129_GY_Curve.jpeg","621_GY_Curve.jpeg","832_GY_Curve.jpeg","832_GY_Curve.jpeg","316_GY_Curve.jpeg","806_GY_Curve.jpeg","833_GY_Curve.jpeg","132_GY_Curve.jpeg",
"711_GY_Curve.jpeg","802_GY_Curve.jpeg","261_GY_Curve.jpeg",]
showImagesMatrix(Growthcurveplot, hSize = 62, wSize = 20,col=2)
PATH = "E:/FS_meeting/FiguresMisc/"
Image(filename = PATH + "Abiotic_parameters.png", width=2000, height=2000)
The values for the atmospheic N slope and Intercept were determined for the area around Ashville, this documentation is available on the Trello Page currently. From there the denitrification rate was determined by lowering the total and mineral N to levels that were appropriate. The decay rates were modified to acheive gently upward sloping curves(Fig1 and 2), and to limit the SOMTC(fig.3) to increasing less than 10% over 50 years. These values may need to be recalibrated with changes in
Figure 1
PATH = "E:/FS_meeting/FiguresMisc/"
Image(filename = PATH + "Nitrogen.jpg", width=1000, height=1000)
Figure 2
PATH = "E:/FS_meeting/FiguresMisc/"
Image(filename = PATH + "Carbon.jpg", width=1000, height=1000)
Figure 3: These are the overall ecosystem characteristics for a single cohort.
##Other Ecosystem stuff
mypath='E:/FS_meeting/FiguresMisc'
Growthcurveplot=["LAI.jpg","Eco.jpg"]
showImagesMatrix(Growthcurveplot, hSize = 100, wSize = 70,col=2)
Using the the growth curves and the values of previous landis functional groups as a estimate we will paramterize the functional groups. Here is an example of the proccesing Quercus Prinus(sp=832) in the Northern Hardwood Group. Using the minimum positive value from the growth curve and its agb value, I grow one cohort of that value with the establishment turned to zero. First we ajdust the LAI factors.
Functional Group LAI parameters: Affect AGB considerably and need to parameterized first.
#Fix this
PATH = "C:/Users/zjrobbin/Desktop/Sapps_SC/PicsofTrees/"
Image(filename = PATH + "Oak_Hickory.png", width=500, height=500)
Citation: Hardiman, B. S., Gough, C. M., Halperin, A., Hofmeister, K. L., Nave, L. E., Bohrer, G., & Curtis, P. S. (2013). Maintaining high rates of carbon storage in old forests: a mechanism linking canopy structure to forest function. Forest Ecology and Management, 298, 111-119.
Once the LAI parameters are in the range of correct we can work on tuning the aboveground biomass.
This is using the growth curves we put together earlier.
Using each run and an ealier run we adjust the parameters to best match the aboveground AGB